Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations11490
Missing cells270587
Missing cells (%)76.0%
Duplicate rows71
Duplicate rows (%)0.6%
Total size in memory2.8 MiB
Average record size in memory256.0 B

Variable types

Numeric13
Categorical8
DateTime3
Text7

Alerts

Category has constant value "Sports" Constant
Dataset has 71 (0.6%) duplicate rowsDuplicates
EngagementID has 6867 (59.8%) missing values Missing
ContentID has 6867 (59.8%) missing values Missing
ContentType has 6867 (59.8%) missing values Missing
Likes has 6867 (59.8%) missing values Missing
EngagementDate has 6867 (59.8%) missing values Missing
CampaignID has 6867 (59.8%) missing values Missing
ViewsClicksCombined has 6867 (59.8%) missing values Missing
JourneyID has 7479 (65.1%) missing values Missing
CustomerID has 4653 (40.5%) missing values Missing
VisitDate has 7479 (65.1%) missing values Missing
Stage has 7479 (65.1%) missing values Missing
Action has 7479 (65.1%) missing values Missing
Duration has 8092 (70.4%) missing values Missing
CustomerName has 11390 (99.1%) missing values Missing
Email has 11390 (99.1%) missing values Missing
Gender has 11390 (99.1%) missing values Missing
Age has 11390 (99.1%) missing values Missing
GeographyID has 11380 (99.0%) missing values Missing
ReviewID has 8764 (76.3%) missing values Missing
ReviewDate has 8764 (76.3%) missing values Missing
Rating has 8764 (76.3%) missing values Missing
ReviewText has 8764 (76.3%) missing values Missing
ProductName has 11470 (99.8%) missing values Missing
Category has 11470 (99.8%) missing values Missing
Price has 11470 (99.8%) missing values Missing
SentimentScore has 10127 (88.1%) missing values Missing
SentimentCategory has 10127 (88.1%) missing values Missing
SentimentBucket has 10127 (88.1%) missing values Missing
Country has 11480 (99.9%) missing values Missing
City has 11480 (99.9%) missing values Missing
EngagementID is uniformly distributed Uniform
JourneyID is uniformly distributed Uniform
ReviewID is uniformly distributed Uniform
Likes has 728 (6.3%) zeros Zeros
SentimentScore has 442 (3.8%) zeros Zeros

Reproduction

Analysis started2024-12-18 23:40:08.906229
Analysis finished2024-12-19 01:45:32.564470
Duration2 hours, 5 minutes and 23.66 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

EngagementID
Real number (ℝ)

Missing  Uniform 

Distinct4623
Distinct (%)100.0%
Missing6867
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean2312
Minimum1
Maximum4623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:46.413502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile232.1
Q11156.5
median2312
Q33467.5
95-th percentile4391.9
Maximum4623
Range4622
Interquartile range (IQR)2311

Descriptive statistics

Standard deviation1334.6895
Coefficient of variation (CV)0.57728784
Kurtosis-1.2
Mean2312
Median Absolute Deviation (MAD)1156
Skewness0
Sum10688376
Variance1781396
MonotonicityStrictly increasing
2024-12-19T03:47:46.757853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3036 1
 
< 0.1%
3088 1
 
< 0.1%
3087 1
 
< 0.1%
3086 1
 
< 0.1%
3085 1
 
< 0.1%
3084 1
 
< 0.1%
3083 1
 
< 0.1%
3082 1
 
< 0.1%
3081 1
 
< 0.1%
3080 1
 
< 0.1%
Other values (4613) 4613
40.1%
(Missing) 6867
59.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4623 1
< 0.1%
4622 1
< 0.1%
4621 1
< 0.1%
4620 1
< 0.1%
4619 1
< 0.1%
4618 1
< 0.1%
4617 1
< 0.1%
4616 1
< 0.1%
4615 1
< 0.1%
4614 1
< 0.1%

ContentID
Real number (ℝ)

Missing 

Distinct50
Distinct (%)1.1%
Missing6867
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean25.42613
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:47.055843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.292613
Coefficient of variation (CV)0.56212301
Kurtosis-1.1925765
Mean25.42613
Median Absolute Deviation (MAD)12
Skewness-0.0043063646
Sum117545
Variance204.27878
MonotonicityNot monotonic
2024-12-19T03:47:47.369465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 114
 
1.0%
35 108
 
0.9%
39 105
 
0.9%
15 104
 
0.9%
41 103
 
0.9%
37 103
 
0.9%
32 102
 
0.9%
46 102
 
0.9%
14 102
 
0.9%
21 101
 
0.9%
Other values (40) 3579
31.1%
(Missing) 6867
59.8%
ValueCountFrequency (%)
1 88
0.8%
2 83
0.7%
3 96
0.8%
4 114
1.0%
5 80
0.7%
6 83
0.7%
7 97
0.8%
8 87
0.8%
9 85
0.7%
10 90
0.8%
ValueCountFrequency (%)
50 70
0.6%
49 93
0.8%
48 90
0.8%
47 85
0.7%
46 102
0.9%
45 93
0.8%
44 99
0.9%
43 78
0.7%
42 68
0.6%
41 103
0.9%

ContentType
Categorical

Missing 

Distinct12
Distinct (%)0.3%
Missing6867
Missing (%)59.8%
Memory size179.5 KiB
Socialmedia
534 
Blog
519 
Newsletter
511 
Video
510 
blog
416 
Other values (7)
2133 

Length

Max length11
Median length10
Mean length7.4806403
Min length4

Characters and Unicode

Total characters34583
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlog
2nd rowBlog
3rd rowvideo
4th rowVideo
5th rownewsletter

Common Values

ValueCountFrequency (%)
Socialmedia 534
 
4.6%
Blog 519
 
4.5%
Newsletter 511
 
4.4%
Video 510
 
4.4%
blog 416
 
3.6%
socialmedia 384
 
3.3%
video 380
 
3.3%
newsletter 359
 
3.1%
SOCIALMEDIA 270
 
2.3%
VIDEO 261
 
2.3%
Other values (2) 479
 
4.2%
(Missing) 6867
59.8%

Length

2024-12-19T03:47:47.620605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
socialmedia 1188
25.7%
blog 1180
25.5%
video 1151
24.9%
newsletter 1104
23.9%

Most occurring characters

ValueCountFrequency (%)
e 4418
 
12.8%
o 2743
 
7.9%
i 2726
 
7.9%
l 2723
 
7.9%
a 1836
 
5.3%
d 1808
 
5.2%
t 1740
 
5.0%
s 1254
 
3.6%
E 1233
 
3.6%
S 1038
 
3.0%
Other values (22) 13064
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4418
 
12.8%
o 2743
 
7.9%
i 2726
 
7.9%
l 2723
 
7.9%
a 1836
 
5.3%
d 1808
 
5.2%
t 1740
 
5.0%
s 1254
 
3.6%
E 1233
 
3.6%
S 1038
 
3.0%
Other values (22) 13064
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4418
 
12.8%
o 2743
 
7.9%
i 2726
 
7.9%
l 2723
 
7.9%
a 1836
 
5.3%
d 1808
 
5.2%
t 1740
 
5.0%
s 1254
 
3.6%
E 1233
 
3.6%
S 1038
 
3.0%
Other values (22) 13064
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4418
 
12.8%
o 2743
 
7.9%
i 2726
 
7.9%
l 2723
 
7.9%
a 1836
 
5.3%
d 1808
 
5.2%
t 1740
 
5.0%
s 1254
 
3.6%
E 1233
 
3.6%
S 1038
 
3.0%
Other values (22) 13064
37.8%

Likes
Real number (ℝ)

Missing  Zeros 

Distinct640
Distinct (%)13.8%
Missing6867
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean114.36859
Minimum0
Maximum1977
Zeros728
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:47.965457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median28
Q3131
95-th percentile517
Maximum1977
Range1977
Interquartile range (IQR)128

Descriptive statistics

Standard deviation205.08386
Coefficient of variation (CV)1.7931834
Kurtosis13.802193
Mean114.36859
Median Absolute Deviation (MAD)28
Skewness3.268792
Sum528726
Variance42059.389
MonotonicityNot monotonic
2024-12-19T03:47:48.232210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 728
 
6.3%
1 238
 
2.1%
2 139
 
1.2%
4 99
 
0.9%
3 96
 
0.8%
5 89
 
0.8%
8 69
 
0.6%
7 68
 
0.6%
6 66
 
0.6%
10 56
 
0.5%
Other values (630) 2975
25.9%
(Missing) 6867
59.8%
ValueCountFrequency (%)
0 728
6.3%
1 238
 
2.1%
2 139
 
1.2%
3 96
 
0.8%
4 99
 
0.9%
5 89
 
0.8%
6 66
 
0.6%
7 68
 
0.6%
8 69
 
0.6%
9 51
 
0.4%
ValueCountFrequency (%)
1977 1
< 0.1%
1938 1
< 0.1%
1760 1
< 0.1%
1534 1
< 0.1%
1530 1
< 0.1%
1526 1
< 0.1%
1493 1
< 0.1%
1434 1
< 0.1%
1420 1
< 0.1%
1405 1
< 0.1%

EngagementDate
Date

Missing 

Distinct1085
Distinct (%)23.5%
Missing6867
Missing (%)59.8%
Memory size179.5 KiB
Minimum2023-01-01 00:00:00
Maximum2025-12-31 00:00:00
2024-12-19T03:47:48.435583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:47:48.655075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CampaignID
Real number (ℝ)

Missing 

Distinct20
Distinct (%)0.4%
Missing6867
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean10.660826
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:48.796059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7705698
Coefficient of variation (CV)0.54128729
Kurtosis-1.2130816
Mean10.660826
Median Absolute Deviation (MAD)5
Skewness-0.033428625
Sum49285
Variance33.299475
MonotonicityNot monotonic
2024-12-19T03:47:48.921823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15 266
 
2.3%
19 254
 
2.2%
7 245
 
2.1%
17 245
 
2.1%
13 240
 
2.1%
20 240
 
2.1%
11 237
 
2.1%
12 236
 
2.1%
3 233
 
2.0%
4 231
 
2.0%
Other values (10) 2196
 
19.1%
(Missing) 6867
59.8%
ValueCountFrequency (%)
1 221
1.9%
2 210
1.8%
3 233
2.0%
4 231
2.0%
5 222
1.9%
6 231
2.0%
7 245
2.1%
8 224
1.9%
9 202
1.8%
10 219
1.9%
ValueCountFrequency (%)
20 240
2.1%
19 254
2.2%
18 218
1.9%
17 245
2.1%
16 230
2.0%
15 266
2.3%
14 219
1.9%
13 240
2.1%
12 236
2.1%
11 237
2.1%

ProductID
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing110
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean10.416432
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:49.047193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile20
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7904462
Coefficient of variation (CV)0.55589534
Kurtosis-1.214684
Mean10.416432
Median Absolute Deviation (MAD)5
Skewness0.018667501
Sum118539
Variance33.529267
MonotonicityNot monotonic
2024-12-19T03:47:49.188187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16 608
 
5.3%
4 602
 
5.2%
2 596
 
5.2%
12 594
 
5.2%
5 594
 
5.2%
7 591
 
5.1%
1 590
 
5.1%
20 577
 
5.0%
15 571
 
5.0%
9 569
 
5.0%
Other values (10) 5488
47.8%
ValueCountFrequency (%)
1 590
5.1%
2 596
5.2%
3 555
4.8%
4 602
5.2%
5 594
5.2%
6 564
4.9%
7 591
5.1%
8 535
4.7%
9 569
5.0%
10 556
4.8%
ValueCountFrequency (%)
20 577
5.0%
19 564
4.9%
18 538
4.7%
17 540
4.7%
16 608
5.3%
15 571
5.0%
14 523
4.6%
13 563
4.9%
12 594
5.2%
11 550
4.8%

ViewsClicksCombined
Text

Missing 

Distinct4586
Distinct (%)99.2%
Missing6867
Missing (%)59.8%
Memory size179.5 KiB
2024-12-19T03:47:49.606614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.3928185
Min length4

Characters and Unicode

Total characters34177
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4550 ?
Unique (%)98.4%

Sample

1st row1883-671
2nd row5280-532
3rd row1905-204
4th row2766-257
5th row5116-1524
ValueCountFrequency (%)
226-10 3
 
0.1%
69-5 2
 
< 0.1%
10-0 2
 
< 0.1%
163-3 2
 
< 0.1%
602-11 2
 
< 0.1%
158-3 2
 
< 0.1%
928-38 2
 
< 0.1%
1159-155 2
 
< 0.1%
6837-2251 2
 
< 0.1%
27-0 2
 
< 0.1%
Other values (4576) 4602
99.5%
2024-12-19T03:47:50.020362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4625
13.5%
- 4623
13.5%
2 3709
10.9%
3 3167
9.3%
4 3007
8.8%
5 2795
8.2%
6 2630
7.7%
7 2526
7.4%
8 2465
7.2%
9 2377
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4625
13.5%
- 4623
13.5%
2 3709
10.9%
3 3167
9.3%
4 3007
8.8%
5 2795
8.2%
6 2630
7.7%
7 2526
7.4%
8 2465
7.2%
9 2377
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4625
13.5%
- 4623
13.5%
2 3709
10.9%
3 3167
9.3%
4 3007
8.8%
5 2795
8.2%
6 2630
7.7%
7 2526
7.4%
8 2465
7.2%
9 2377
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4625
13.5%
- 4623
13.5%
2 3709
10.9%
3 3167
9.3%
4 3007
8.8%
5 2795
8.2%
6 2630
7.7%
7 2526
7.4%
8 2465
7.2%
9 2377
7.0%

JourneyID
Real number (ℝ)

Missing  Uniform 

Distinct3932
Distinct (%)98.0%
Missing7479
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean1973.4538
Minimum1
Maximum3932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:50.192602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile198.5
Q1986.5
median1977
Q32958.5
95-th percentile3739.5
Maximum3932
Range3931
Interquartile range (IQR)1972

Descriptive statistics

Standard deviation1137.1585
Coefficient of variation (CV)0.57622757
Kurtosis-1.2046707
Mean1973.4538
Median Absolute Deviation (MAD)986
Skewness-0.0073827951
Sum7915523
Variance1293129.4
MonotonicityNot monotonic
2024-12-19T03:47:50.349607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
494 2
 
< 0.1%
3559 2
 
< 0.1%
404 2
 
< 0.1%
3531 2
 
< 0.1%
1592 2
 
< 0.1%
443 2
 
< 0.1%
1533 2
 
< 0.1%
520 2
 
< 0.1%
3609 2
 
< 0.1%
3401 2
 
< 0.1%
Other values (3922) 3991
34.7%
(Missing) 7479
65.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3932 1
< 0.1%
3931 1
< 0.1%
3930 2
< 0.1%
3929 1
< 0.1%
3928 1
< 0.1%
3927 1
< 0.1%
3926 1
< 0.1%
3925 1
< 0.1%
3924 1
< 0.1%
3923 1
< 0.1%

CustomerID
Real number (ℝ)

Missing 

Distinct100
Distinct (%)1.5%
Missing4653
Missing (%)40.5%
Infinite0
Infinite (%)0.0%
Mean52.502413
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:50.584718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128
median53
Q378
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.463493
Coefficient of variation (CV)0.54213686
Kurtosis-1.188781
Mean52.502413
Median Absolute Deviation (MAD)25
Skewness-0.082053873
Sum358959
Variance810.17045
MonotonicityNot monotonic
2024-12-19T03:47:50.898745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 105
 
0.9%
84 98
 
0.9%
93 91
 
0.8%
22 90
 
0.8%
92 86
 
0.7%
70 86
 
0.7%
82 85
 
0.7%
88 84
 
0.7%
24 84
 
0.7%
46 83
 
0.7%
Other values (90) 5945
51.7%
(Missing) 4653
40.5%
ValueCountFrequency (%)
1 46
0.4%
2 61
0.5%
3 63
0.5%
4 59
0.5%
5 52
0.5%
6 77
0.7%
7 60
0.5%
8 62
0.5%
9 42
0.4%
10 65
0.6%
ValueCountFrequency (%)
100 71
0.6%
99 61
0.5%
98 59
0.5%
97 61
0.5%
96 59
0.5%
95 74
0.6%
94 73
0.6%
93 91
0.8%
92 86
0.7%
91 69
0.6%

VisitDate
Date

Missing 

Distinct1065
Distinct (%)26.6%
Missing7479
Missing (%)65.1%
Memory size179.5 KiB
Minimum2023-01-01 00:00:00
Maximum2025-12-30 00:00:00
2024-12-19T03:47:51.040114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:47:51.196726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Stage
Categorical

Missing 

Distinct6
Distinct (%)0.1%
Missing7479
Missing (%)65.1%
Memory size179.5 KiB
Homepage
1692 
ProductPage
1370 
Checkout
748 
homepage
 
85
productpage
 
74

Length

Max length11
Median length8
Mean length9.0800299
Min length8

Characters and Unicode

Total characters36420
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheckout
2nd rowCheckout
3rd rowProductPage
4th rowCheckout
5th rowHomepage

Common Values

ValueCountFrequency (%)
Homepage 1692
 
14.7%
ProductPage 1370
 
11.9%
Checkout 748
 
6.5%
homepage 85
 
0.7%
productpage 74
 
0.6%
checkout 42
 
0.4%
(Missing) 7479
65.1%

Length

2024-12-19T03:47:51.338114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:51.463485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
homepage 1777
44.3%
productpage 1444
36.0%
checkout 790
19.7%

Most occurring characters

ValueCountFrequency (%)
e 5788
15.9%
o 4011
11.0%
a 3221
8.8%
g 3221
8.8%
P 2740
 
7.5%
c 2276
 
6.2%
u 2234
 
6.1%
t 2234
 
6.1%
p 1925
 
5.3%
m 1777
 
4.9%
Other values (6) 6993
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5788
15.9%
o 4011
11.0%
a 3221
8.8%
g 3221
8.8%
P 2740
 
7.5%
c 2276
 
6.2%
u 2234
 
6.1%
t 2234
 
6.1%
p 1925
 
5.3%
m 1777
 
4.9%
Other values (6) 6993
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5788
15.9%
o 4011
11.0%
a 3221
8.8%
g 3221
8.8%
P 2740
 
7.5%
c 2276
 
6.2%
u 2234
 
6.1%
t 2234
 
6.1%
p 1925
 
5.3%
m 1777
 
4.9%
Other values (6) 6993
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5788
15.9%
o 4011
11.0%
a 3221
8.8%
g 3221
8.8%
P 2740
 
7.5%
c 2276
 
6.2%
u 2234
 
6.1%
t 2234
 
6.1%
p 1925
 
5.3%
m 1777
 
4.9%
Other values (6) 6993
19.2%

Action
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing7479
Missing (%)65.1%
Memory size179.5 KiB
View
2116 
Click
1082 
Drop-off
613 
Purchase
 
200

Length

Max length8
Median length4
Mean length5.0805285
Min length4

Characters and Unicode

Total characters20378
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrop-off
2nd rowDrop-off
3rd rowView
4th rowDrop-off
5th rowClick

Common Values

ValueCountFrequency (%)
View 2116
 
18.4%
Click 1082
 
9.4%
Drop-off 613
 
5.3%
Purchase 200
 
1.7%
(Missing) 7479
65.1%

Length

2024-12-19T03:47:51.636118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:51.777104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
view 2116
52.8%
click 1082
27.0%
drop-off 613
 
15.3%
purchase 200
 
5.0%

Most occurring characters

ValueCountFrequency (%)
i 3198
15.7%
e 2316
11.4%
V 2116
10.4%
w 2116
10.4%
c 1282
 
6.3%
o 1226
 
6.0%
f 1226
 
6.0%
C 1082
 
5.3%
l 1082
 
5.3%
k 1082
 
5.3%
Other values (9) 3652
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3198
15.7%
e 2316
11.4%
V 2116
10.4%
w 2116
10.4%
c 1282
 
6.3%
o 1226
 
6.0%
f 1226
 
6.0%
C 1082
 
5.3%
l 1082
 
5.3%
k 1082
 
5.3%
Other values (9) 3652
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3198
15.7%
e 2316
11.4%
V 2116
10.4%
w 2116
10.4%
c 1282
 
6.3%
o 1226
 
6.0%
f 1226
 
6.0%
C 1082
 
5.3%
l 1082
 
5.3%
k 1082
 
5.3%
Other values (9) 3652
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3198
15.7%
e 2316
11.4%
V 2116
10.4%
w 2116
10.4%
c 1282
 
6.3%
o 1226
 
6.0%
f 1226
 
6.0%
C 1082
 
5.3%
l 1082
 
5.3%
k 1082
 
5.3%
Other values (9) 3652
17.9%

Duration
Real number (ℝ)

Missing 

Distinct291
Distinct (%)8.6%
Missing8092
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean156.51324
Minimum10
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:51.918472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile23
Q186
median157
Q3228
95-th percentile285
Maximum300
Range290
Interquartile range (IQR)142

Descriptive statistics

Standard deviation83.213779
Coefficient of variation (CV)0.53167245
Kurtosis-1.1793027
Mean156.51324
Median Absolute Deviation (MAD)71
Skewness-0.04341425
Sum531832
Variance6924.5331
MonotonicityNot monotonic
2024-12-19T03:47:52.059580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201 21
 
0.2%
23 20
 
0.2%
136 20
 
0.2%
56 20
 
0.2%
108 20
 
0.2%
217 19
 
0.2%
216 19
 
0.2%
255 19
 
0.2%
21 18
 
0.2%
69 18
 
0.2%
Other values (281) 3204
 
27.9%
(Missing) 8092
70.4%
ValueCountFrequency (%)
10 15
0.1%
11 8
0.1%
12 8
0.1%
13 14
0.1%
14 12
0.1%
15 11
0.1%
16 14
0.1%
17 10
0.1%
18 12
0.1%
19 4
 
< 0.1%
ValueCountFrequency (%)
300 8
0.1%
299 7
0.1%
298 10
0.1%
297 8
0.1%
296 12
0.1%
295 13
0.1%
294 11
0.1%
293 9
0.1%
292 12
0.1%
291 11
0.1%

CustomerName
Text

Missing 

Distinct100
Distinct (%)100.0%
Missing11390
Missing (%)99.1%
Memory size179.5 KiB
2024-12-19T03:47:52.232207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length12.95
Min length10

Characters and Unicode

Total characters1295
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowEmma Anderson
2nd rowSarah Brown
3rd rowRobert Hernandez
4th rowDavid Garcia
5th rowEmma Miller
ValueCountFrequency (%)
gonzalez 10
 
5.0%
emma 10
 
5.0%
hernandez 10
 
5.0%
rodriguez 9
 
4.5%
david 9
 
4.5%
olivia 9
 
4.5%
john 8
 
4.0%
james 8
 
4.0%
thomas 8
 
4.0%
jane 7
 
3.5%
Other values (22) 112
56.0%
2024-12-19T03:47:52.561445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 138
 
10.7%
e 106
 
8.2%
i 102
 
7.9%
100
 
7.7%
n 88
 
6.8%
o 80
 
6.2%
l 77
 
5.9%
r 64
 
4.9%
s 53
 
4.1%
m 51
 
3.9%
Other values (29) 436
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 138
 
10.7%
e 106
 
8.2%
i 102
 
7.9%
100
 
7.7%
n 88
 
6.8%
o 80
 
6.2%
l 77
 
5.9%
r 64
 
4.9%
s 53
 
4.1%
m 51
 
3.9%
Other values (29) 436
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 138
 
10.7%
e 106
 
8.2%
i 102
 
7.9%
100
 
7.7%
n 88
 
6.8%
o 80
 
6.2%
l 77
 
5.9%
r 64
 
4.9%
s 53
 
4.1%
m 51
 
3.9%
Other values (29) 436
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 138
 
10.7%
e 106
 
8.2%
i 102
 
7.9%
100
 
7.7%
n 88
 
6.8%
o 80
 
6.2%
l 77
 
5.9%
r 64
 
4.9%
s 53
 
4.1%
m 51
 
3.9%
Other values (29) 436
33.7%

Email
Text

Missing 

Distinct100
Distinct (%)100.0%
Missing11390
Missing (%)99.1%
Memory size179.5 KiB
2024-12-19T03:47:52.765312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length29
Median length27
Mean length24.95
Min length22

Characters and Unicode

Total characters2495
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowemma.anderson@example.com
2nd rowsarah.brown@example.com
3rd rowrobert.hernandez@example.com
4th rowdavid.garcia@example.com
5th rowemma.miller@example.com
ValueCountFrequency (%)
sarah.martinez@example.com 1
 
1.0%
michael.smith@example.com 1
 
1.0%
sarah.brown@example.com 1
 
1.0%
robert.hernandez@example.com 1
 
1.0%
david.garcia@example.com 1
 
1.0%
emma.miller@example.com 1
 
1.0%
daniel.rodriguez@example.com 1
 
1.0%
laura.miller@example.com 1
 
1.0%
james.gonzalez@example.com 1
 
1.0%
emily.thomas@example.com 1
 
1.0%
Other values (90) 90
90.0%
2024-12-19T03:47:53.125677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 323
12.9%
m 269
10.8%
a 248
 
9.9%
. 200
 
8.0%
l 190
 
7.6%
o 189
 
7.6%
c 117
 
4.7%
p 111
 
4.4%
i 106
 
4.2%
x 105
 
4.2%
Other values (15) 637
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 323
12.9%
m 269
10.8%
a 248
 
9.9%
. 200
 
8.0%
l 190
 
7.6%
o 189
 
7.6%
c 117
 
4.7%
p 111
 
4.4%
i 106
 
4.2%
x 105
 
4.2%
Other values (15) 637
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 323
12.9%
m 269
10.8%
a 248
 
9.9%
. 200
 
8.0%
l 190
 
7.6%
o 189
 
7.6%
c 117
 
4.7%
p 111
 
4.4%
i 106
 
4.2%
x 105
 
4.2%
Other values (15) 637
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 323
12.9%
m 269
10.8%
a 248
 
9.9%
. 200
 
8.0%
l 190
 
7.6%
o 189
 
7.6%
c 117
 
4.7%
p 111
 
4.4%
i 106
 
4.2%
x 105
 
4.2%
Other values (15) 637
25.5%

Gender
Categorical

Missing 

Distinct2
Distinct (%)2.0%
Missing11390
Missing (%)99.1%
Memory size179.5 KiB
Female
54 
Male
46 

Length

Max length6
Median length6
Mean length5.08
Min length4

Characters and Unicode

Total characters508
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 54
 
0.5%
Male 46
 
0.4%
(Missing) 11390
99.1%

Length

2024-12-19T03:47:53.297908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:53.413164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
female 54
54.0%
male 46
46.0%

Most occurring characters

ValueCountFrequency (%)
e 154
30.3%
a 100
19.7%
l 100
19.7%
F 54
 
10.6%
m 54
 
10.6%
M 46
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 154
30.3%
a 100
19.7%
l 100
19.7%
F 54
 
10.6%
m 54
 
10.6%
M 46
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 154
30.3%
a 100
19.7%
l 100
19.7%
F 54
 
10.6%
m 54
 
10.6%
M 46
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 154
30.3%
a 100
19.7%
l 100
19.7%
F 54
 
10.6%
m 54
 
10.6%
M 46
 
9.1%

Age
Real number (ℝ)

Missing 

Distinct45
Distinct (%)45.0%
Missing11390
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean41.99
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:53.533412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q131
median41
Q353
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.259142
Coefficient of variation (CV)0.33958423
Kurtosis-1.0129934
Mean41.99
Median Absolute Deviation (MAD)12
Skewness0.23025626
Sum4199
Variance203.32313
MonotonicityNot monotonic
2024-12-19T03:47:53.690018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26 5
 
< 0.1%
41 5
 
< 0.1%
25 4
 
< 0.1%
31 4
 
< 0.1%
53 4
 
< 0.1%
43 4
 
< 0.1%
38 3
 
< 0.1%
34 3
 
< 0.1%
24 3
 
< 0.1%
48 3
 
< 0.1%
Other values (35) 62
 
0.5%
(Missing) 11390
99.1%
ValueCountFrequency (%)
18 2
 
< 0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
22 3
< 0.1%
23 1
 
< 0.1%
24 3
< 0.1%
25 4
< 0.1%
26 5
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
69 2
< 0.1%
68 1
 
< 0.1%
67 3
< 0.1%
66 1
 
< 0.1%
64 1
 
< 0.1%
63 3
< 0.1%
62 2
< 0.1%
61 1
 
< 0.1%
60 3
< 0.1%
59 1
 
< 0.1%

GeographyID
Real number (ℝ)

Missing 

Distinct10
Distinct (%)9.1%
Missing11380
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean5.3363636
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:53.816158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8163262
Coefficient of variation (CV)0.52776129
Kurtosis-1.0855717
Mean5.3363636
Median Absolute Deviation (MAD)2
Skewness0.1306785
Sum587
Variance7.9316931
MonotonicityNot monotonic
2024-12-19T03:47:53.925895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 19
 
0.2%
5 13
 
0.1%
2 12
 
0.1%
10 11
 
0.1%
1 11
 
0.1%
6 10
 
0.1%
7 10
 
0.1%
8 9
 
0.1%
9 9
 
0.1%
3 6
 
0.1%
(Missing) 11380
99.0%
ValueCountFrequency (%)
1 11
0.1%
2 12
0.1%
3 6
 
0.1%
4 19
0.2%
5 13
0.1%
6 10
0.1%
7 10
0.1%
8 9
0.1%
9 9
0.1%
10 11
0.1%
ValueCountFrequency (%)
10 11
0.1%
9 9
0.1%
8 9
0.1%
7 10
0.1%
6 10
0.1%
5 13
0.1%
4 19
0.2%
3 6
 
0.1%
2 12
0.1%
1 11
0.1%

ReviewID
Real number (ℝ)

Missing  Uniform 

Distinct1363
Distinct (%)50.0%
Missing8764
Missing (%)76.3%
Infinite0
Infinite (%)0.0%
Mean682
Minimum1
Maximum1363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:54.066883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile69
Q1341.25
median682
Q31022.75
95-th percentile1295
Maximum1363
Range1362
Interquartile range (IQR)681.5

Descriptive statistics

Standard deviation393.53629
Coefficient of variation (CV)0.57703269
Kurtosis-1.200001
Mean682
Median Absolute Deviation (MAD)341
Skewness0
Sum1859132
Variance154870.81
MonotonicityNot monotonic
2024-12-19T03:47:54.223892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
907 2
 
< 0.1%
915 2
 
< 0.1%
914 2
 
< 0.1%
913 2
 
< 0.1%
912 2
 
< 0.1%
911 2
 
< 0.1%
910 2
 
< 0.1%
909 2
 
< 0.1%
908 2
 
< 0.1%
906 2
 
< 0.1%
Other values (1353) 2706
 
23.6%
(Missing) 8764
76.3%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
1363 2
< 0.1%
1362 2
< 0.1%
1361 2
< 0.1%
1360 2
< 0.1%
1359 2
< 0.1%
1358 2
< 0.1%
1357 2
< 0.1%
1356 2
< 0.1%
1355 2
< 0.1%
1354 2
< 0.1%

ReviewDate
Date

Missing 

Distinct793
Distinct (%)29.1%
Missing8764
Missing (%)76.3%
Memory size179.5 KiB
Minimum2023-01-01 00:00:00
Maximum2025-12-31 00:00:00
2024-12-19T03:47:54.380505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:47:54.537503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rating
Categorical

Missing 

Distinct5
Distinct (%)0.2%
Missing8764
Missing (%)76.3%
Memory size179.5 KiB
4.0
862 
5.0
818 
3.0
580 
2.0
306 
1.0
160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8178
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 862
 
7.5%
5.0 818
 
7.1%
3.0 580
 
5.0%
2.0 306
 
2.7%
1.0 160
 
1.4%
(Missing) 8764
76.3%

Length

2024-12-19T03:47:54.678491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:54.915509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 862
31.6%
5.0 818
30.0%
3.0 580
21.3%
2.0 306
 
11.2%
1.0 160
 
5.9%

Most occurring characters

ValueCountFrequency (%)
. 2726
33.3%
0 2726
33.3%
4 862
 
10.5%
5 818
 
10.0%
3 580
 
7.1%
2 306
 
3.7%
1 160
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2726
33.3%
0 2726
33.3%
4 862
 
10.5%
5 818
 
10.0%
3 580
 
7.1%
2 306
 
3.7%
1 160
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2726
33.3%
0 2726
33.3%
4 862
 
10.5%
5 818
 
10.0%
3 580
 
7.1%
2 306
 
3.7%
1 160
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2726
33.3%
0 2726
33.3%
4 862
 
10.5%
5 818
 
10.0%
3 580
 
7.1%
2 306
 
3.7%
1 160
 
2.0%

ReviewText
Text

Missing 

Distinct102
Distinct (%)3.7%
Missing8764
Missing (%)76.3%
Memory size179.5 KiB
2024-12-19T03:47:55.086630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length65
Median length55
Mean length38.768892
Min length23

Characters and Unicode

Total characters105684
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage experience, nothing special.
2nd rowThe quality is top-notch.
3rd rowFive stars for the quick delivery.
4th rowGood quality, but could be cheaper.
5th rowAverage experience, nothing special.
ValueCountFrequency (%)
the 1492
 
10.2%
product 658
 
4.5%
quality 598
 
4.1%
was 596
 
4.1%
is 500
 
3.4%
very 438
 
3.0%
but 390
 
2.7%
not 338
 
2.3%
for 280
 
1.9%
customer 246
 
1.7%
Other values (70) 9050
62.0%
2024-12-19T03:47:55.432094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25100
23.8%
e 9218
 
8.7%
t 6548
 
6.2%
i 5318
 
5.0%
a 4778
 
4.5%
r 4658
 
4.4%
o 4582
 
4.3%
s 4004
 
3.8%
u 3720
 
3.5%
h 3546
 
3.4%
Other values (31) 34212
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25100
23.8%
e 9218
 
8.7%
t 6548
 
6.2%
i 5318
 
5.0%
a 4778
 
4.5%
r 4658
 
4.4%
o 4582
 
4.3%
s 4004
 
3.8%
u 3720
 
3.5%
h 3546
 
3.4%
Other values (31) 34212
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25100
23.8%
e 9218
 
8.7%
t 6548
 
6.2%
i 5318
 
5.0%
a 4778
 
4.5%
r 4658
 
4.4%
o 4582
 
4.3%
s 4004
 
3.8%
u 3720
 
3.5%
h 3546
 
3.4%
Other values (31) 34212
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25100
23.8%
e 9218
 
8.7%
t 6548
 
6.2%
i 5318
 
5.0%
a 4778
 
4.5%
r 4658
 
4.4%
o 4582
 
4.3%
s 4004
 
3.8%
u 3720
 
3.5%
h 3546
 
3.4%
Other values (31) 34212
32.4%

ProductName
Text

Missing 

Distinct20
Distinct (%)100.0%
Missing11470
Missing (%)99.8%
Memory size179.5 KiB
2024-12-19T03:47:55.620343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length11.25
Min length5

Characters and Unicode

Total characters225
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st rowRunning Shoes
2nd rowFitness Tracker
3rd rowYoga Mat
4th rowDumbbells
5th rowSoccer Ball
ValueCountFrequency (%)
helmet 2
 
5.7%
ski 1
 
2.9%
soccer 1
 
2.9%
glove 1
 
2.9%
football 1
 
2.9%
basketball 1
 
2.9%
tennis 1
 
2.9%
racket 1
 
2.9%
ball 1
 
2.9%
shoes 1
 
2.9%
Other values (24) 24
68.6%
2024-12-19T03:47:55.918344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 23
 
10.2%
e 21
 
9.3%
o 16
 
7.1%
a 15
 
6.7%
15
 
6.7%
s 12
 
5.3%
i 10
 
4.4%
t 10
 
4.4%
n 9
 
4.0%
b 9
 
4.0%
Other values (28) 85
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 23
 
10.2%
e 21
 
9.3%
o 16
 
7.1%
a 15
 
6.7%
15
 
6.7%
s 12
 
5.3%
i 10
 
4.4%
t 10
 
4.4%
n 9
 
4.0%
b 9
 
4.0%
Other values (28) 85
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 23
 
10.2%
e 21
 
9.3%
o 16
 
7.1%
a 15
 
6.7%
15
 
6.7%
s 12
 
5.3%
i 10
 
4.4%
t 10
 
4.4%
n 9
 
4.0%
b 9
 
4.0%
Other values (28) 85
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 23
 
10.2%
e 21
 
9.3%
o 16
 
7.1%
a 15
 
6.7%
15
 
6.7%
s 12
 
5.3%
i 10
 
4.4%
t 10
 
4.4%
n 9
 
4.0%
b 9
 
4.0%
Other values (28) 85
37.8%

Category
Categorical

Constant  Missing 

Distinct1
Distinct (%)5.0%
Missing11470
Missing (%)99.8%
Memory size179.5 KiB
Sports
20 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters120
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSports
2nd rowSports
3rd rowSports
4th rowSports
5th rowSports

Common Values

ValueCountFrequency (%)
Sports 20
 
0.2%
(Missing) 11470
99.8%

Length

2024-12-19T03:47:56.074960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:56.169474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sports 20
100.0%

Most occurring characters

ValueCountFrequency (%)
S 20
16.7%
p 20
16.7%
o 20
16.7%
r 20
16.7%
t 20
16.7%
s 20
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 20
16.7%
p 20
16.7%
o 20
16.7%
r 20
16.7%
t 20
16.7%
s 20
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 20
16.7%
p 20
16.7%
o 20
16.7%
r 20
16.7%
t 20
16.7%
s 20
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 20
16.7%
p 20
16.7%
o 20
16.7%
r 20
16.7%
t 20
16.7%
s 20
16.7%

Price
Real number (ℝ)

Missing 

Distinct20
Distinct (%)100.0%
Missing11470
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean205.4055
Minimum26.21
Maximum485.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.5 KiB
2024-12-19T03:47:56.279219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum26.21
5-th percentile35.577
Q144.2625
median210.215
Q3288.4125
95-th percentile472.97
Maximum485.32
Range459.11
Interquartile range (IQR)244.15

Descriptive statistics

Standard deviation149.46182
Coefficient of variation (CV)0.72764271
Kurtosis-0.85330248
Mean205.4055
Median Absolute Deviation (MAD)129.305
Skewness0.42295537
Sum4108.11
Variance22338.834
MonotonicityNot monotonic
2024-12-19T03:47:56.404585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
340.2 1
 
< 0.1%
173.83 1
 
< 0.1%
42.8 1
 
< 0.1%
275.43 1
 
< 0.1%
259.4 1
 
< 0.1%
410.17 1
 
< 0.1%
472.32 1
 
< 0.1%
145.97 1
 
< 0.1%
37.56 1
 
< 0.1%
81.59 1
 
< 0.1%
Other values (10) 10
 
0.1%
(Missing) 11470
99.8%
ValueCountFrequency (%)
26.21 1
< 0.1%
36.07 1
< 0.1%
37.56 1
< 0.1%
41.26 1
< 0.1%
42.8 1
< 0.1%
44.75 1
< 0.1%
81.59 1
< 0.1%
145.97 1
< 0.1%
173.83 1
< 0.1%
196.68 1
< 0.1%
ValueCountFrequency (%)
485.32 1
< 0.1%
472.32 1
< 0.1%
410.17 1
< 0.1%
340.2 1
< 0.1%
327.36 1
< 0.1%
275.43 1
< 0.1%
262.32 1
< 0.1%
259.4 1
< 0.1%
225.12 1
< 0.1%
223.75 1
< 0.1%

SentimentScore
Real number (ℝ)

Missing  Zeros 

Distinct15
Distinct (%)1.1%
Missing10127
Missing (%)88.1%
Infinite0
Infinite (%)0.0%
Mean0.19265899
Minimum-0.6288
Maximum0.8016
Zeros442
Zeros (%)3.8%
Negative335
Negative (%)2.9%
Memory size179.5 KiB
2024-12-19T03:47:56.498711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.6288
5-th percentile-0.3089
Q10
median0
Q30.6696
95-th percentile0.8016
Maximum0.8016
Range1.4304
Interquartile range (IQR)0.6696

Descriptive statistics

Standard deviation0.40310841
Coefficient of variation (CV)2.0923416
Kurtosis-1.2822935
Mean0.19265899
Median Absolute Deviation (MAD)0.2382
Skewness0.25010414
Sum262.5942
Variance0.16249639
MonotonicityNot monotonic
2024-12-19T03:47:56.624470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 442
 
3.8%
0.2382 122
 
1.1%
0.6997 114
 
1.0%
-0.1695 113
 
1.0%
0.8016 105
 
0.9%
0.6696 99
 
0.9%
-0.3089 91
 
0.8%
-0.2617 73
 
0.6%
0.5106 56
 
0.5%
0.7773 54
 
0.5%
Other values (5) 94
 
0.8%
(Missing) 10127
88.1%
ValueCountFrequency (%)
-0.6288 9
 
0.1%
-0.5423 9
 
0.1%
-0.4767 31
 
0.3%
-0.3089 91
 
0.8%
-0.2617 73
 
0.6%
-0.2263 9
 
0.1%
-0.1695 113
 
1.0%
0 442
3.8%
0.2382 122
 
1.1%
0.5106 56
 
0.5%
ValueCountFrequency (%)
0.8016 105
 
0.9%
0.7773 54
 
0.5%
0.7351 36
 
0.3%
0.6997 114
 
1.0%
0.6696 99
 
0.9%
0.5106 56
 
0.5%
0.2382 122
 
1.1%
0 442
3.8%
-0.1695 113
 
1.0%
-0.2263 9
 
0.1%

SentimentCategory
Categorical

Missing 

Distinct5
Distinct (%)0.4%
Missing10127
Missing (%)88.1%
Memory size179.5 KiB
Positive
840 
Negative
226 
Mixed Negative
196 
Mixed Positive
86 
Neutral
 
15

Length

Max length14
Median length8
Mean length9.2303742
Min length7

Characters and Unicode

Total characters12581
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMixed Negative
2nd rowPositive
3rd rowPositive
4th rowMixed Positive
5th rowMixed Negative

Common Values

ValueCountFrequency (%)
Positive 840
 
7.3%
Negative 226
 
2.0%
Mixed Negative 196
 
1.7%
Mixed Positive 86
 
0.7%
Neutral 15
 
0.1%
(Missing) 10127
88.1%

Length

2024-12-19T03:47:56.764965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:56.890329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
positive 926
56.3%
negative 422
25.7%
mixed 282
 
17.1%
neutral 15
 
0.9%

Most occurring characters

ValueCountFrequency (%)
i 2556
20.3%
e 2067
16.4%
t 1363
10.8%
v 1348
10.7%
P 926
 
7.4%
s 926
 
7.4%
o 926
 
7.4%
a 437
 
3.5%
N 437
 
3.5%
g 422
 
3.4%
Other values (7) 1173
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2556
20.3%
e 2067
16.4%
t 1363
10.8%
v 1348
10.7%
P 926
 
7.4%
s 926
 
7.4%
o 926
 
7.4%
a 437
 
3.5%
N 437
 
3.5%
g 422
 
3.4%
Other values (7) 1173
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2556
20.3%
e 2067
16.4%
t 1363
10.8%
v 1348
10.7%
P 926
 
7.4%
s 926
 
7.4%
o 926
 
7.4%
a 437
 
3.5%
N 437
 
3.5%
g 422
 
3.4%
Other values (7) 1173
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2556
20.3%
e 2067
16.4%
t 1363
10.8%
v 1348
10.7%
P 926
 
7.4%
s 926
 
7.4%
o 926
 
7.4%
a 437
 
3.5%
N 437
 
3.5%
g 422
 
3.4%
Other values (7) 1173
9.3%

SentimentBucket
Categorical

Missing 

Distinct4
Distinct (%)0.3%
Missing10127
Missing (%)88.1%
Memory size179.5 KiB
0.0 to 0.49
564 
0.5 to 1.0
464 
-0.49 to 0.0
317 
-1.0 to -0.5
 
18

Length

Max length12
Median length11
Mean length10.905356
Min length10

Characters and Unicode

Total characters14864
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.49 to 0.0
2nd row0.0 to 0.49
3rd row0.0 to 0.49
4th row0.0 to 0.49
5th row-0.49 to 0.0

Common Values

ValueCountFrequency (%)
0.0 to 0.49 564
 
4.9%
0.5 to 1.0 464
 
4.0%
-0.49 to 0.0 317
 
2.8%
-1.0 to -0.5 18
 
0.2%
(Missing) 10127
88.1%

Length

2024-12-19T03:47:57.031710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T03:47:57.157087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
to 1363
33.3%
0.0 881
21.5%
0.49 881
21.5%
0.5 482
 
11.8%
1.0 482
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 3607
24.3%
. 2726
18.3%
2726
18.3%
t 1363
 
9.2%
o 1363
 
9.2%
4 881
 
5.9%
9 881
 
5.9%
5 482
 
3.2%
1 482
 
3.2%
- 353
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3607
24.3%
. 2726
18.3%
2726
18.3%
t 1363
 
9.2%
o 1363
 
9.2%
4 881
 
5.9%
9 881
 
5.9%
5 482
 
3.2%
1 482
 
3.2%
- 353
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3607
24.3%
. 2726
18.3%
2726
18.3%
t 1363
 
9.2%
o 1363
 
9.2%
4 881
 
5.9%
9 881
 
5.9%
5 482
 
3.2%
1 482
 
3.2%
- 353
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3607
24.3%
. 2726
18.3%
2726
18.3%
t 1363
 
9.2%
o 1363
 
9.2%
4 881
 
5.9%
9 881
 
5.9%
5 482
 
3.2%
1 482
 
3.2%
- 353
 
2.4%

Country
Text

Missing 

Distinct10
Distinct (%)100.0%
Missing11480
Missing (%)99.9%
Memory size179.5 KiB
2024-12-19T03:47:57.319093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length6.5
Mean length6.7
Min length2

Characters and Unicode

Total characters67
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st rowUK
2nd rowGermany
3rd rowFrance
4th rowSpain
5th rowItaly
ValueCountFrequency (%)
uk 1
10.0%
germany 1
10.0%
france 1
10.0%
spain 1
10.0%
italy 1
10.0%
netherlands 1
10.0%
belgium 1
10.0%
sweden 1
10.0%
switzerland 1
10.0%
austria 1
10.0%
2024-12-19T03:47:57.612085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8
 
11.9%
a 7
 
10.4%
n 6
 
9.0%
r 5
 
7.5%
i 4
 
6.0%
l 4
 
6.0%
t 4
 
6.0%
S 3
 
4.5%
d 3
 
4.5%
u 2
 
3.0%
Other values (17) 21
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8
 
11.9%
a 7
 
10.4%
n 6
 
9.0%
r 5
 
7.5%
i 4
 
6.0%
l 4
 
6.0%
t 4
 
6.0%
S 3
 
4.5%
d 3
 
4.5%
u 2
 
3.0%
Other values (17) 21
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8
 
11.9%
a 7
 
10.4%
n 6
 
9.0%
r 5
 
7.5%
i 4
 
6.0%
l 4
 
6.0%
t 4
 
6.0%
S 3
 
4.5%
d 3
 
4.5%
u 2
 
3.0%
Other values (17) 21
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8
 
11.9%
a 7
 
10.4%
n 6
 
9.0%
r 5
 
7.5%
i 4
 
6.0%
l 4
 
6.0%
t 4
 
6.0%
S 3
 
4.5%
d 3
 
4.5%
u 2
 
3.0%
Other values (17) 21
31.3%

City
Text

Missing 

Distinct10
Distinct (%)100.0%
Missing11480
Missing (%)99.9%
Memory size179.5 KiB
2024-12-19T03:47:57.800343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8.5
Mean length6.5
Min length4

Characters and Unicode

Total characters65
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st rowLondon
2nd rowBerlin
3rd rowParis
4th rowMadrid
5th rowRome
ValueCountFrequency (%)
london 1
10.0%
berlin 1
10.0%
paris 1
10.0%
madrid 1
10.0%
rome 1
10.0%
amsterdam 1
10.0%
brussels 1
10.0%
stockholm 1
10.0%
zurich 1
10.0%
vienna 1
10.0%
2024-12-19T03:47:58.145605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 6
 
9.2%
n 5
 
7.7%
e 5
 
7.7%
i 5
 
7.7%
s 5
 
7.7%
o 5
 
7.7%
d 4
 
6.2%
a 4
 
6.2%
m 4
 
6.2%
l 3
 
4.6%
Other values (14) 19
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 6
 
9.2%
n 5
 
7.7%
e 5
 
7.7%
i 5
 
7.7%
s 5
 
7.7%
o 5
 
7.7%
d 4
 
6.2%
a 4
 
6.2%
m 4
 
6.2%
l 3
 
4.6%
Other values (14) 19
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 6
 
9.2%
n 5
 
7.7%
e 5
 
7.7%
i 5
 
7.7%
s 5
 
7.7%
o 5
 
7.7%
d 4
 
6.2%
a 4
 
6.2%
m 4
 
6.2%
l 3
 
4.6%
Other values (14) 19
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 6
 
9.2%
n 5
 
7.7%
e 5
 
7.7%
i 5
 
7.7%
s 5
 
7.7%
o 5
 
7.7%
d 4
 
6.2%
a 4
 
6.2%
m 4
 
6.2%
l 3
 
4.6%
Other values (14) 19
29.2%

Interactions

2024-12-19T03:07:18.588674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:43.387024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:50.021725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:12.132669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:43:06.354854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:44:49.312873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:47:42.279306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:52:02.528115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:58:15.076725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:06:36.742865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:17:18.103518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:30:48.186529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:47:23.318257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:08:59.830219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:44.000072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:51.071792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:14.797933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:43:12.251411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:44:59.159508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:47:58.898647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:52:26.219966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:58:48.957274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:07:20.805973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:18:14.254915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:31:58.546073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:48:47.279771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:10:42.491036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:44.407242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:52.075537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:17.651638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:43:18.287991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:45:10.141951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:48:16.551888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:52:52.107327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:59:22.764192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:08:05.054847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:19:11.663619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:33:09.264901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:50:12.752186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:12:27.240372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:44.704762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:45:22.272882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:48:34.781906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:53:17.493436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:59:57.667318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:08:51.133486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:20:09.931766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:34:21.107465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:51:39.120528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:14:11.903435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:44.986736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:54.239414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:23.783549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:45:34.221220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:48:52.367726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:53:43.208990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:00:34.530915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:09:38.488636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:21:09.407340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:35:34.083946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:53:06.473697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:15:59.458262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:45.237904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:55.478101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:27.499335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:49:11.105935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T02:22:09.511974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:36:48.108382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:54:36.636843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:17:47.094050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:45.676872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:56.795393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:31.544327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:43:45.760628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:45:59.499030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:49:30.707312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:54:37.459661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:01:50.748780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:11:14.814222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:23:10.950572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:38:02.229707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:56:07.332853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:19:36.101994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:45.974877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:58.552452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:36.091059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:43:53.742068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:46:13.500777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:49:50.041486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:55:07.167052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:02:29.549655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T02:24:13.595402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:39:19.051197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:57:39.298450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:41:46.319629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:00.449613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:40.481388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:44:02.632375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:46:27.377706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:50:09.452365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:55:35.860039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:03:08.780631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T02:40:36.579718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:59:13.408752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:23:18.168655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:46.821978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:02.518578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:45.154829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:56:06.845308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:03:48.552269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:42:50.140533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:44:19.989263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:46:55.868063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:50:53.111601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:56:37.486236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:04:29.886278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T03:27:06.412479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:48.359063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:07.082337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:55.629169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:44:29.225316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:47:11.077449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T01:57:09.284933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:05:12.205471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:15:29.687645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:28:31.789613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:44:36.481424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:04:01.185364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:29:04.152220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:41:49.190149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:42:09.608291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:43:00.835802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:44:38.946563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:47:26.396274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:51:38.569350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T01:57:41.773322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:05:54.233106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:16:23.064921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:29:38.942275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T02:45:59.303520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T03:05:39.299131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-12-19T03:31:03.182104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T03:33:03.588244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-19T03:35:05.836542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

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